As shown in the top of Fig. REF , given the set of category label texts (e.g. “armchair”, “rocking chair”, and “sofa chair”), we first extract text features from category labels following Eq.(REF ). The \(d\) -dimensional category text features are denoted as \(C=\lbrace c_{1},\dots ,c_{k_2}\rbrace  \in \mathbb {R}^{k_2 \times d}\) , where \(c_{i}\)  is the sentence-level feature of category \(i\) , and \(k_2\)  is the category number (e.g. 524 in Nr3D [1]} and 607 in Sr3D [1]}). Then classification logits \(p_{i} \in \mathbb {R}^{k_2}\)  of object \(i\)  are calculated by the inner product of category text features \(C\)  and the object feature \(x_{i}\) . We adopt a cross-entropy loss to supervise \(p_{i}\) , denotes as \(\mathcal {L}_{obj}\) .
